Micron‐Textured Ambipolar Photodetectors Enabling ∼20 µs Photonic Adaptation

ABSTRACT The rapid development of artificial intelligence calls for compact, flexible, and intelligent photodetectors. However, strong background illumination can overwhelm weak optical signals, while downstream compensation imposes substantial computational burdens, thereby introducing considerable processing latency. Here, we demonstrate an ultrathin (8 µm), mechanically flexible ambipolar electronic vision (AEV) system that suppresses background optical interference directly at the photodetection front end. Symmetric back‐to‐back Schottky barriers minimize the net vertical internal electric field, while a self‐assembled donor‐enriched micron‐scale texture generates a small surface‐potential gradient (∼10 mV) to balance carrier transport and collection. This mechanism stabilizes illumination‐direction‐dependent photocurrent polarity near zero bias, reduces the minimum irradiance required for ambipolar operation from 175 mW cm −2 to 0.1 mW cm −2 , corresponding to an approximately 1750‐fold reduction. Through the device‐level superposition of oppositely signed photocurrents, the ultrathin, flexible AEV array directly suppresses background‐induced photocurrent offsets and exhibits bidirectional response times of ∼20 µs, enabling rapid photonic adaptation. The ambipolar response remains stable after array integration and 3500 bending cycles. Nearly 100% recognition accuracy is maintained under strong background illumination of ∼11 mW cm −2 , establishing a device‐level strategy for rapid and reliable visual perception in flexible and conformable electronic vision systems.

Authors

Institutions

Publication Details

Journal
Advanced Materials
Published
2026-09-25
DOI
https://doi.org/10.1002/adma.75139
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Micron‐Textured Ambipolar Photodetectors Enabling ∼20 µs Photonic Adaptation

Xiaosheng Fang, Ziqing Li, Gang Wang, Limin Wu et al.
Advanced Materials
Neural Networks and Reservoir Computing
article

Micron‐Textured Ambipolar Photodetectors Enabling ∼20 µs Photonic Adaptation

Xiaosheng Fang, Ziqing Li, Gang Wang, Limin Wu, Ming Deng, Tingting Yan, Yarong Gu, Bobo Li, Junhao Chu
article en

Abstract

ABSTRACT The rapid development of artificial intelligence calls for compact, flexible, and intelligent photodetectors. However, strong background illumination can overwhelm weak optical signals, while downstream compensation imposes substantial computational burdens, thereby introducing considerable processing latency. Here, we demonstrate an ultrathin (8 µm), mechanically flexible ambipolar electronic vision (AEV) system that suppresses background optical interference directly at the photodetection front end. Symmetric back‐to‐back Schottky barriers minimize the net vertical internal electric field, while a self‐assembled donor‐enriched micron‐scale texture generates a small surface‐potential gradient (∼10 mV) to balance carrier transport and collection. This mechanism stabilizes illumination‐direction‐dependent photocurrent polarity near zero bias, reduces the minimum irradiance required for ambipolar operation from 175 mW cm −2 to 0.1 mW cm −2 , corresponding to an approximately 1750‐fold reduction. Through the device‐level superposition of oppositely signed photocurrents, the ultrathin, flexible AEV array directly suppresses background‐induced photocurrent offsets and exhibits bidirectional response times of ∼20 µs, enabling rapid photonic adaptation. The ambipolar response remains stable after array integration and 3500 bending cycles. Nearly 100% recognition accuracy is maintained under strong background illumination of ∼11 mW cm −2 , establishing a device‐level strategy for rapid and reliable visual perception in flexible and conformable electronic vision systems.

Advanced Materials
Fudan University (CN), Inner Mongolia University (CN), Wuhan University (CN)
Openalex Percentile: Top 9%
Neural Networks and Reservoir Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Micron‐Textured Ambipolar Photodetectors Enabling ∼20 µs Photonic Adaptation — Xiaosheng Fang, Ziqing Li, et al. · Advanced Materials (2026) | TGRS Research Map | TGRS